A modeler's manifesto: Synthesizing modeling best practices with social science frameworks to support critical approaches to data science

Abstract

In the face of the "crisis of reproducibility" and the rise of "big data" with its associated issues, modeling needs to be practiced more critically and less automatically. Many modelers are discussing better modeling practices, but to address questions about the transparency, equity, and relevance of modeling, we also need the theoretical grounding of social science and the tools of critical theory. I have therefore synthesized recent work by modelers on better practices for modeling with social science literature (especially feminist science and technology studies) to offer a "modeler’s manifesto": a set of applied practices and framings for critical modeling approaches. Broadly, these practices involve 1) giving greater context to scientific modeling through extended methods sections, appendices, and companion articles, clarifying quantitative and qualitative reasoning and process; 2) greater collaboration in scientific modeling via triangulation with different data sources, gaining feedback from interdisciplinary teams, and viewing uncertainty as openness and invitation for dialogue; and 3) directly engaging with justice and ethics by watching for and mitigating unequal power dynamics in projects, facing the impacts and implications of the work throughout the process rather than only afterwards, and seeking opportunities to collaborate directly with people impacted by the modeling.